Explore global development with R

In this exercise, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.

Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks within this script.

Submit to Github this script with solutions and its knitted version.
Submit to Brightspace a document with the (1) URL of this script and its knitted version in Github (2) answer of 250 words and a (3) visualisation of the Final Question

Get the necessary packages

First, start with installing and activating the relevant packages tidyverse, gganimate, and gapminder if you do not have them already. Pay attention to what warning messages you get when installing gganimate, as your computer might need other packages than gifski and av

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.0.4     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Look at the data and tackle the tasks

First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.

str(gapminder)

## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ year     : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num [1:1704] 28.8 30.3 32 34 36.1 ...
##  $ pop      : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num [1:1704] 779 821 853 836 740 ...

unique(gapminder$year)

##  [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007

head(gapminder)

## # A tibble: 6 × 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.

# Filtrer for året 2007
gdp_2007 <- gapminder %>%
  filter(year == 2007) %>%
  select(country, gdpPercap) 

# De 5 lande med den højeste GDP per capita
top_5 <- gdp_2007 %>%
  arrange(desc(gdpPercap)) %>%
  head(5)

# Vis resultaterne
print("Top 5 lande med højeste GDP per capita:")

## [1] "Top 5 lande med højeste GDP per capita:"

print(top_5)

## # A tibble: 5 × 2
##   country       gdpPercap
##   <fct>             <dbl>
## 1 Norway           49357.
## 2 Kuwait           47307.
## 3 Singapore        47143.
## 4 United States    42952.
## 5 Ireland          40676.

The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.

Let’s plot all the countries in 1952.

theme_set(theme_bw())  # set theme to white background for better visibility

ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() +
  ggtitle("Figure 01")+
  geom_point(aes(colour = continent))+
  geom_text(aes(label=country),hjust=0,vjust=0)+
  theme_bw()+
  xlab("GDP per capita")+
  ylab("Life expectancy")+
  ggtitle("Global Development 1952")

…

We see an interesting spread with an outlier to the right. Explore who it is so you can answer question 2 below!

Next, you can generate a similar plot for 2007 and compare the differences

options(scipen=999)
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
   scale_x_log10()+
  ggtitle("Figure 02")+
  geom_point(aes(colour = continent))+
  geom_text(aes(label=country),hjust=0,vjust=0)+
  theme_bw()+
  xlab("GDP per capita")+
  ylab("Life expectancy")+
  ggtitle("Global Development 2007")

…

The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.

Questions for the static figures:

Answer: why does it make sense to have a log10 scale (scale_x_log10()) on the x axis? (hint: try to comment it out and observe the result) # Det ændrer på x-aksens værdier, til at være tættere. Det gør det muligt at forstå dataen bedre.
Answer: In Figure 1: Who is the outlier (the richest country in 1952) far right on the x axis? # Det er Kuwait, hvilket kunne ses ved at putte navne på landene ved at bruge “geom_text(aes(label=country),hjust=0,vjust=0)”.
Fix Figures 1 and 2: Differentiate the continents by color, and fix the axis labels and units to be more legible (Hint: the 2.50e+08 is so called “scientific notation”. You want to eliminate it.)
Answer: What are the five richest countries in the world in 2007? 1: Norway
2: Kuwait
3: Singapore
4: United States
5: Ireland

Make it move!

The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. Beware that there may be other packages your operating system needs in order to glue interim images into an animation or video. Read the messages when installing the package.

Also, there are two ways of animating the gapminder ggplot.

Option 1: Animate using transition_states()

The first step is to create the object-to-be-animated

options(scipen=999)
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()+
   geom_point(aes(colour = continent))
anim

…

This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the bottom right ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the visual inside an html file.

options(scipen=999)
anim + transition_states(year, 
                      transition_length = 1,
                      state_length = 1)+
  geom_point(aes(colour=continent))+
  theme_minimal() +
  transition_time(year) +             
  ease_aes('linear')

…

Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.

Option 2 Animate using transition_time()

This option smooths the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.

options(scipen=999)
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop,colour=continent)) +
  geom_point() +
  scale_x_log10(labels=scales::label_comma())+
  transition_time(year)+
  geom_point(alpha = 0.7, show.legend = FALSE) +
  labs(
    title = 'Global Development in {frame_time}',
    x = 'GDP per capita', 
    y = 'Life expectancy'
  ) +
  theme_minimal() +
  transition_time(year) +             
  ease_aes('linear')
  
anim2

The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.

Now, choose one of the animation options and get it to work. You may need to troubleshoot your installation of gganimate and other packages

Tasks for the animations:

Can you add a title to one or both of the animations above that will change in sync with the animation? # Jeg har givet animation nr. 2 et navn og angivet årstal der ændrer sig, med udvkilingen.
Can you make the axes’ labels and units more readable? Consider expanding the abbreviated labels as well as the scientific notation in the legend and x axis to whole numbers. Also, differentiate the countries from different continents by color

Final Question

Is the world a better place today than it was in the year you were born? Answer this question using the gapminder data. Define better either as more prosperous, more free, more healthy, or suggest another measure that you can get from gapminder. Submit a 250 word answer with an illustration to Brightspace. Include a URL in your Brightspace submission that links to the coded solutions in Github. [Hint: if you wish to have more data than is in the filtered gapminder, you can load either the gapminder_unfiltered dataset or download more historical data at https://www.gapminder.org/data/ ]

options(scipen=999)
ggplot(subset(gapminder, year == 2002), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
   scale_x_log10()+
  ggtitle("Figure 02")+
  geom_point(aes(colour = continent))+
  geom_text(aes(label=country),hjust=0,vjust=0)+
  theme_bw()+
  xlab("GDP per capita")+
  ylab("Life expectancy")+
  ggtitle("Global Development 2002")

Make Countries Move

Thilde Laue Jacobsen

14 marts 2025